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Comorbidity analysis results: general info
Comorbidity analysis with ID: 1097522786ba756b8328377f0589fdd3, executed in date: 2018-11-14 15:55:28
The total number of diagnosis pairs considered in this analysis is 10965 of which: 3655 pairs in the gender independent comorbidity analysis 3655 pairs in the comorbidity analysis of females 3655 pairs in the gender independent comorbidity analysis of males.

From the following link you can download:
  • a self-contained ZIP file including this stand-alone web visualization and the results of the comorbidity analysis in CSV format: here
  • a CSV file including all the results of the comorbidity analysis: here
  • a CSV file including all the results of the comorbidity analysis for female patients: here
  • a CSV file including all the results of the comorbidity analysis for male patients: here
The results you're visualizing in this page are temporarly stored in the local files:
  • /home/ronzano/comorbidity4web/results/result_1097522786ba756b8328377f0589fdd3.html
  • /home/ronzano/comorbidity4web/results/executionSummaryPage_1097522786ba756b8328377f0589fdd3.html
  • result_1097522786ba756b8328377f0589fdd3_ALL.csv
  • /home/ronzano/comorbidity4web/results/result_1097522786ba756b8328377f0589fdd3_FEMALE.csv
  • result_1097522786ba756b8328377f0589fdd3_MALE.csv
  • /home/ronzano/comorbidity4web/results/result_1097522786ba756b8328377f0589fdd3.zip
.
You can trigger the immediate deletion of all the results of this comorbidity analysis by clicking here.
Input parameters
The following set of tabs collects the parameters used to perform the comorbidity analysis:

Patient Data input settings

  • Patient Data file name:  patients_comorbidity4j_example.csv
  • Patient Data file size:  0.0695018768310547 Mb approx.
  • Column Name of patientData file, column patient_id:  PATIENT_ID
  • Column Name of patientData file, column patient_dateBirth:  BIRTHDATE
  • Column Name of patientData file, column patient_gender:  GENDER
  • Column Name of patientData file, column patient_facet_1:  ETHNICITY
  • Patient Data date format:  yyyy-MM-dd
  • Patient Data column separator:  ' '
  • Patient Data text delimiter:  N
  • Patient Data has first row header:  Yes
  • Patient Data is OMOP Common Data Model compliant:  No

Visit Data input settings

  • Visit Data file name:  visits_comorbidity4j_example.csv
  • Visit Data file size:  8 Mb approx.
  • Column Name of Visit Data file, column patient_id:  PATIENT_ID
  • Column Name of Visit Data file, column visit_id:  VISIT_ID
  • Column Name of Visit Data file, column visitStartDate:  VISIT_DATE
  • Visit Data date format:  yyyy-MM-dd'T'HH:mm:ss'Z'
  • Visit Data column separator:  ' '
  • Visit Data text delimiter:  N
  • Visit Data has first row header:  Yes
  • Visit Data is OMOP Common Data Model compliant:  No

Diagnosis Data input settings

  • Diagnosis Data file name:  ---
  • Diagnosis Data file size:  0 Mb approx.
  • Column Name of Diagnosis Data file, column patient_id:  PATIENT_ID
  • Column Name of Diagnosis Data file, column visit_id:  VISIT_ID
  • Column Name of Diagnosis Data file, column diagnosis_code:  DIAGNOSIS_CODE
  • Diagnosis Data column separator:  ' '
  • Diagnosis Data text delimiter:  N
  • Diagnosis Data has first row header:  Yes
  • Diagnosis Data is OMOP Common Data Model compliant:  No

Diagnosis Description input settings

  • Diagnosis Description Data file name:  diagnosis_descriptions_comorbidity4j_example.csv
  • Diagnosis Description Data file size:  0 Mb approx.
  • Column Name of Diagnosis Description file, column diagnosis_code:  DIAGNOSIS_DESCRIPTION
  • Column Name of Diagnosis Description file, column diagnosis_description:  DIAGNOSIS_CODE
  • Diagnosis Description Data column separator:  ' '
  • Diagnosis Description Data text delimiter:  N
  • Diagnosis Description Data has first row header:  Yes

Disease Grouping settings

  • Number of groups of diseases defined:  0

Disease Pairing settings

  • Number of disease pairing patterns defined:  0
  • Number of disease pairs to study:  0 ALL PAIRS OF DIAGNOSES WILL BE STUDIED FOR COMORBIDITY.
  • Comments:  ALL PAIRS OF DIAGNOSES WILL BE STUDIED FOR COMORBIDITY.

Other parameters

  • Enable multithreading:  4
  • Patient age computation approach (used if the patient age filter is enabled):  LAST_DIAGNOSTIC
  • P-value adjust approach:  BENJAMINI_YEKUTIELI
  • Odds ratio confidence interval:  0.95
  • Female identifier to compute sex ratio value:  F
  • Male identifier to compute sex ratio value:  M

Filters used to generate the analysis

  • Patient filter:  ---
  • Time directionality filter:  ---
  • Scores filter:  ---
Processing log
Below you can find the log of the comorbidity data loading and analysis:

Global log messages

exec_993877751 ***** Number of diseases pairs to study 3655.
exec_993877751 ***** Number of diseases of the dataset that are included in at least one disease pair to study 86 (over 86 diseases in input data).
exec_993877751 ***** 
exec_993877751 ***** Top-5 and botom-5 diseases paired with more / less diseases to study comorbidities:
exec_993877751 *****        > Disease 1 over 85 participating in more pairs > DISEASE ID 233.4 (51) participates in 85 disease pairs to study for comorbidity.
exec_993877751 *****        > Disease 2 over 85 participating in more pairs > DISEASE ID 277.7 (3) participates in 84 disease pairs to study for comorbidity.
exec_993877751 *****        > Disease 3 over 85 participating in more pairs > DISEASE ID 239.5 (52) participates in 83 disease pairs to study for comorbidity.
exec_993877751 *****        > Disease 4 over 85 participating in more pairs > DISEASE ID 296.2 (54) participates in 82 disease pairs to study for comorbidity.
exec_993877751 *****        > Disease 5 over 85 participating in more pairs > DISEASE ID V22.2 (13) participates in 81 disease pairs to study for comorbidity.
exec_993877751 *****        > Disease 80 over 85 participating in more pairs > DISEASE ID 715.96 (59) participates in 6 disease pairs to study for comorbidity.
exec_993877751 *****        > Disease 81 over 85 participating in more pairs > DISEASE ID 790.6 (7) participates in 5 disease pairs to study for comorbidity.
exec_993877751 *****        > Disease 82 over 85 participating in more pairs > DISEASE ID 850.5 (38) participates in 4 disease pairs to study for comorbidity.
exec_993877751 *****        > Disease 83 over 85 participating in more pairs > DISEASE ID 949.3 (68) participates in 3 disease pairs to study for comorbidity.
exec_993877751 *****        > Disease 84 over 85 participating in more pairs > DISEASE ID 949.2 (45) participates in 2 disease pairs to study for comorbidity.
exec_993877751 *****        > Disease 85 over 85 participating in more pairs > DISEASE ID 949.1 (32) participates in 1 disease pairs to study for comorbidity.
exec_993877751 ***** Comorbidity dataset created, including  86 diseases.
exec_993877751 **************************************************
exec_993877751
Patient data overview
Below you can find a collection of charts useful to explore the features of the group of patient data you provided as input for comorbidity analysis. To explore the results of the comorbidity analysis over this set of patients, refer to the complete list of comorbidity pairs.
Important: you defined the following set of criteria to select / filter patients:  --- for comorbidity analysis.
The charts shown below provide an overview of all the patient data of your input dataset / files WITHOUT applying any filter!
Patients by age charts
Patients by birth-date charts
Patients by disease charts
Visits by disease charts
Sex ratio analysis
The following table shows the sex ratio of all pairs of diseases analyzed.
BA Sex Ratio column: given all individuals with disease A: (i) > 0 means prevalence of disease B in females; (ii) < 0 means prevalence of disease B in males; (iii) close to 0 means disease B is equally likely for females and males.
AB Sex Ratio column: given all individuals with disease B: (i) > 0 means prevalence of disease A in females; (ii) < 0 means prevalence of disease A in males; (iii) close to 0 means disease A is equally likely for females and males.
Comorbidity list
The following table shows the results of the comorbidity analysis: each row describe a pair of diseases.
The input dataset has been filtered by means of the following criteria (if any):
  • Patient filter:  ---
  • Time directionality filter:  ---
  • Scores filter:  ---
resulting in - disease pairs shown in the following table and evaluated with respect to the relevance of their comorbidity (see Input parameter section to review the input data used).
You can click on the header of each column to order disease pairs with respect to a specific comorbidity score. It is possible to resize columns as well as to change the order of each column by clicking on its header and draggin it.
You're considering both FEMALE and MALE
Interactive visualizations
From this tab you can interactively define criteria to further filter the complete list of comorbidity pairs.
The filtered set of comorbidity pairs will be visualized by means of a table and a heatmap to visually suport data exploration and analysis.
You're considering both FEMALE and MALE
Due to the high number of pairs that match filters (X pairs selected by the filter over X), your interactive visualizations could be difficult to explore and slow to generate.

As a general rule, we suggest to keep the number of disease pairs to interactively explore under 250 otherwise browser may slow down considerably.

As a consequence, we suggest to click on the 'Set more restrictive filters' button, change you filter to make them more restrictive, then trgger data visualization again.

-

Interactive filter setting

Click on the checkbox to enable / disable each filter, then modify the filter parameters by clicking on the pencil icon.
Change filter P-value cut-off:
(use mouse or left / right arrows to change slider value)
Current selection:
Value range:
Change filter Adj. p-value cut-off:
(use mouse or left / right arrows to change slider value)
Current selection:
Value range:
Change filter Rel. risk cut-off:
(use mouse or left / right arrows to change slider value)
Current selection:
Value range:
Change filter Phi cut-off:
(use mouse or left / right arrows to change slider value)
Current selection:
Value range:
Change filter Comor. score cut-off:
(use mouse or left / right arrows to change slider value)
Current selection:
Value range:
Change filter Odds ratio cut-off:
(use mouse or left / right arrows to change slider value)
Current selection:
Value range:
Change filter Num. of patients cut-off:
(use mouse or left / right arrows to change slider value)
Current selection:
Value range:
Pairs with diagnosis:  SELECT No disease selected
Parameter to visualize in the heatmap:

Results:

Please, properly set filters and other visualization parameters in the 'Interactive filter setting' box, then click 'Analyze!'
List of applied filters: No filter applied No filter applied No filter applied No filter applied No filter applied No filter applied No filter applied No filter applied
You're considering both FEMALE and MALE

Table of filtered comorbidities:
Heatmap visualization of filtered comorbidities:

Network visualization of filtered comorbidities
(zoom with mouse scroll wheel and drag & drop nodes to explore the network):

Heatmap visualization of sex ratio of the filtered disease pairs:
For each comorbidity pair shown in the heatmap, a sex-ratio value close to zero indicates that the co-occurrence of the disease on the x-axis, given a patient suffering the corresponding disease on the y-axis, is equally likely for males and females. A positive (negative) value of sex ratio indicate that the co-occurrence of the disease on the x-axis, given a patient suffering the corresponding disease on the y-axis, is more likely for females (males). Thus if the sex ratio has positive values the diagnosis of the x-axis disease in patients that have been diagnosed with the y-axis disease is more likely in females than males.

Info and contact
This analysis and visualizations have been performed by relying on Comorbidity4j, a java library useful to carry out comorbidity analyses. Detailed documentation and practical examples are available online at:
http://comorbidity4j.readthedocs.io/


Comorbidity4j is developed and maintained by the:
Integrative Biomedical Informatics Group
part of the Research Programme on Biomedical Informatics (GRIB), a joint research programme of the Hospital del Mar Medical Research Institute (IMIM) and the Department of Experimental and Health Sciences of the Universitat Pompeu Fabra in Barcelona.

If you need any support in using the tool or if you want to provide us with feedback and suggestions, please send an email to francesco<DOT>ronzano<AT>upf<DOT>edu.
Please, select a diagnosis: